Out[7]:

<Axes: xlabel='alcohol', ylabel='total'>

In [ ]:

Inference: The scatterplot helps visualize the relationship between alcohol involvement and total crashes.

1. Histplot

   In [8]:

   sns.histplot(x="total",bins=20, kde=True,data=df) Out[8]:

   <Axes: xlabel='total', ylabel='Count'>

   In [ ]:

   Inference:The histplot allows you to draw the following inferences about the distribution of total crashes

   
   

2. Boxplot

In [9]:

sns.boxplot(df) Out[9]:

>

<Axes:

In [ ]:

Inference: This boxplot helps visualize the relationship between all columns and their p rices.

.

1. Countplot

   In [10]:

   sns.countplot(x="abbrev",data=df) Out[10]:

   <Axes: xlabel='abbrev', ylabel='count'>

   
   

   In [ ]:

   Inference:In this countplot we can see that relation between states and count of crashes occured in each state.

   
   

   .

2. Jointplot

In [11]:

sns.jointplot(x="alcohol",y="speeding",data=df) Out[11]:

90>

<seaborn.axisgrid.JointGrid at 0x233c8e8dd

In [ ]:

Inference:The joint plot above displays the relationship between the "alcohol" and "speed ing" variables.

1. Relplot

   In [12]:

   sns.relplot(x="alcohol",y="speeding",data=df,height=6,hue="speeding") Out[12]:

   <seaborn.axisgrid.FacetGrid at 0x233c922edd0>

   
   

   
   

   In [ ]:

   Inference:The relplot allows us to visually assess the relationship between alcohol invo lvement and speeding involvement in car crashes

   
   

2. Lineplot

   In [13]:

   sns.lineplot(x="abbrev",y="total",data=df,marker="o") Out[13]:

   <Axes: xlabel='abbrev', ylabel='total'>

   
   

   In [ ]:

   Inference:The plot visualizes how the total number of crashes varies from one state to a nother state.

   
   

3. Barplot

   In [14]:

   >

   sns.barplot(x="abbrev",y="total",data=df) Out[14]:

   <Axes: xlabel='abbrev', ylabel='total'

   
   

   In [ ]:

   Inference:The bar plot shows the average total crashes by state.

   
   

4. Heatmap

In [22]:

corr=df.corr() corr

C:\Users\murra\AppData\Local\Temp\ipykernel_27992\1661191605.py:1: FutureWarning: The def ault value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silen ce this warning.

corr=df.corr() Out[22]:

total

speeding

alcohol

not_distracted

no_previous

ins_pre iu

ins_losses

total speeding alcohol not_distracted no_previous ins_premium ins_losses

total

total 1.000000 0.611548 0.852613 0.827560 0.956179 -0.199702 -0.036011

speeding

speeding 0.611548 1.000000 0.669719 0.588010 0.571976 -0.077675 -0.065928

alcohol

alcohol 0.852613 0.669719 1.000000 0.732816 0.783520 -0.170612 -0.112547

not_distracted

not_distracted 0.827560 0.588010 0.732816 1.000000 0.747307 -0.174856 -0.075970

no_previous

no_previous 0.956179 0.571976 0.783520 0.747307 1.000000 -0.156895 -0.006359

ins_pre iu

ins_premium -0.199702 -0.077675 -0.170612 -0.174856 -0.156895 1.000000 0.623116

ins_losses

ins_losses -0.036011 -0.065928 -0.112547 -0.075970 -0.006359 0.623116 1.000000

In [23]:

sns.heatmap(corr,annot=True) Out[23]:

>

<Axes:

In [ ]:

Inference:

1. Darker colors indicate stronger correlations, while lighter colors indicate weaker or no correlations.

2. The diagonal of the heatmap is always 1 because it represents the correlation of a va riable with itself (perfect correlation).

In [ ]:

In [ ]:

In [ ]: